用机器学习自动调控薄膜沉积，精准逼近目标光学性能。
A Machine Learning Approach Capturing Hidden Parameters in Autonomous Thin-Film Deposition
- 通过校准层和主动学习，捕捉隐藏参数影响
- 2.3次尝试内实现反射率误差低于2.5%
- 适合材料自动化研发与智能制造领域
将机器学习与机器人技术融入薄膜沉积，正推动材料发现与优化的变革。然而，在实现沉积、表征与决策的全自动化循环中仍面临挑战，且薄膜生长对基底状态、腔室条件等隐藏参数敏感，会削弱模型性能。本文展示了一套全自动物理气相沉积系统，结合原位光学光谱、高通量机器人样品处理及高斯过程回归模型。通过校准层补偿隐藏参数变化，并采用主动学习算法优化参数空间探索，系统在平均2.3次尝试内制备出反射功率比与目标偏差小于2.5%的银薄膜。该方法显著减少时间与人力成本，展示了机器学习驱动自动化在加速材料开发中的潜力。
原文摘要 · Abstract (English)
The integration of machine learning and robotics into thin film deposition is transforming material discovery and optimization. However, challenges remain in achieving a fully autonomous cycle of deposition, characterization, and decision-making. Additionally, the inherent sensitivity of thin film growth to hidden parameters such as substrate conditions and chamber conditions can compromise the performance of machine learning models. In this work, we demonstrate a fully autonomous physical vapor deposition system that combines in-situ optical spectroscopy, a high-throughput robotic sample handling system, and Gaussian Process Regression models. By employing a calibration layer to account for hidden parameter variations and an active learning algorithm to optimize the exploration of the parameter space, the system fabricates silver thin films with optical reflected power ratios within 2.5% of the target in an average of 2.3 attempts. This approach significantly reduces the time and labor required for thin film deposition, showcasing the potential of machine learning-driven automation in accelerating material development.
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